How Not to Lose Friends and Alienate People
If you want to keep a secret, you must also hide it from yourself.
— George Orwell 1984
In order to learn (or teach) data science you need data (surprise!). The best libraries often come with a toy dataset to illustrate examples of how the code works. However, nothing can replace an actual, non-trivial dataset for a tutorial or . . .
For the mind does not require filling like a bottle, but rather, like wood, it only requires kindling to create in it an impulse to think independently and an ardent desire for the truth.
— Plutarch On Listening to Lectures
The impulse to ingest more data is our first and most powerful instinct. Born with billions of neurons, as . . .
This post will cover a few things needed to quickly implement a fast, principled method for machine learning model parameter tuning. There are two common methods of parameter tuning: grid search and random search. Each have their pros and cons. Grid search is slow but effective at searching the whole search space, while random search is fast, . . .
Discrete events pervade our daily lives. These include phone calls, online transactions, and heartbeats. Despite the simplicity of discrete event data, it’s hard to visualize many events over a long time period without hiding details about shorter timescales.
The plot below illustrates this problem. It shows the number of website visits made . . .
We are living through an information revolution. Like any economic revolution, it has had a transformative effect on society, academia, and business. The present revolution, driven as it is by networked communication systems and the Internet, is unique in that it has created a surplus of a valuable new material - data - and transformed us all . . .
Posted in: data products
A central lesson of science is that to understand complex issues (or even simple ones), we must try to free our minds of dogma and to guarantee the freedom to publish, to contradict, and to experiment.
As data scientists, it's easy . . .
Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. In this way, sentiment analysis can be seen as a method to quantify qualitative data with some sentiment score. While sentiment is largely subjective, sentiment . . .